CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers

Point cloud completion task aims to predict the missing part of incomplete point clouds and generate complete point clouds with details. In this paper, we propose a novel point cloud completion network, CompleteDT, which is based on the transformer. CompleteDT can learn features within neighborhoods and explore the relationship among these neighborhoods. By sampling the incomplete point cloud to obtain point clouds with different resolutions, we extract features from these point clouds in a self-guided manner, while converting these features into a series of $patches$ based on the geometrical structure. To facilitate transformers to leverage sufficient information about point clouds, we provide a plug-and-play module named Relation-Augment Attention Module (RAA), consisting of Point Cross-Attention Module (PCA) and Point Dense Multi-Scale Attention Module (PDMA). These two modules can enhance the ability to learn features within Patches and consider the correlation among these Patches. Thus, RAA enables to learn structures of incomplete point clouds and contribute to infer the local details of complete point clouds generated. In addition, we predict the complete shape from $patches$ with an efficient generation module, namely, Multi-resolution Point Fusion Module (MPF). MPF gradually generates complete point clouds from $patches$, and updates $patches$ based on these generated point clouds. Experimental results show that our method largely outperforms the state-of-the-art methods.